{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pipenv install --skip-lock pmdarima\n",
    "#!pipenv install --skip-lock matplotlib\n",
    "#!pipenv install --skip-lock nb-black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check pmdarima\n",
    "from pmdarima.arima import auto_arima"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA\n",
    "\n",
    "ARIMA (AutoRegressive Integrated Moving Average) is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values.\n",
    "\n",
    "ARIMA models explain a time series based on its own past values, basically its own lags and the lagged forecast errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                var nbb_unformatted_code = \"from IPython.core.debugger import set_trace\\n\\n%load_ext nb_black\\n\\nimport pandas as pd\\nimport numpy as np\\nimport os\\nimport matplotlib.pyplot as plt\\nimport time\\n\\nplt.style.use(style=\\\"seaborn\\\")\\n%matplotlib inline\";\n",
       "                var nbb_formatted_code = \"from IPython.core.debugger import set_trace\\n\\n%load_ext nb_black\\n\\nimport pandas as pd\\nimport numpy as np\\nimport os\\nimport matplotlib.pyplot as plt\\nimport time\\n\\nplt.style.use(style=\\\"seaborn\\\")\\n%matplotlib inline\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.debugger import set_trace\n",
    "\n",
    "%load_ext nb_black\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "plt.style.use(style=\"seaborn\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"df = pd.read_csv(\\\"data/MSFT-1Y-Hourly.csv\\\")\";\n",
       "                var nbb_formatted_code = \"df = pd.read_csv(\\\"data/MSFT-1Y-Hourly.csv\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/MSFT-1Y-Hourly.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>average</th>\n",
       "      <th>barCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-08-07 14:30:00</td>\n",
       "      <td>133.80</td>\n",
       "      <td>133.83</td>\n",
       "      <td>131.82</td>\n",
       "      <td>132.89</td>\n",
       "      <td>35647</td>\n",
       "      <td>132.701</td>\n",
       "      <td>17523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-08-07 15:00:00</td>\n",
       "      <td>132.87</td>\n",
       "      <td>135.20</td>\n",
       "      <td>132.64</td>\n",
       "      <td>134.75</td>\n",
       "      <td>48757</td>\n",
       "      <td>134.043</td>\n",
       "      <td>26974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-08-07 16:00:00</td>\n",
       "      <td>134.74</td>\n",
       "      <td>134.92</td>\n",
       "      <td>133.52</td>\n",
       "      <td>133.88</td>\n",
       "      <td>28977</td>\n",
       "      <td>134.147</td>\n",
       "      <td>17853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-08-07 17:00:00</td>\n",
       "      <td>133.89</td>\n",
       "      <td>134.06</td>\n",
       "      <td>133.07</td>\n",
       "      <td>133.90</td>\n",
       "      <td>21670</td>\n",
       "      <td>133.618</td>\n",
       "      <td>13497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-08-07 18:00:00</td>\n",
       "      <td>133.89</td>\n",
       "      <td>135.24</td>\n",
       "      <td>133.83</td>\n",
       "      <td>134.83</td>\n",
       "      <td>22648</td>\n",
       "      <td>134.653</td>\n",
       "      <td>12602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date    open    high     low   close  volume  average  \\\n",
       "0  2019-08-07 14:30:00  133.80  133.83  131.82  132.89   35647  132.701   \n",
       "1  2019-08-07 15:00:00  132.87  135.20  132.64  134.75   48757  134.043   \n",
       "2  2019-08-07 16:00:00  134.74  134.92  133.52  133.88   28977  134.147   \n",
       "3  2019-08-07 17:00:00  133.89  134.06  133.07  133.90   21670  133.618   \n",
       "4  2019-08-07 18:00:00  133.89  135.24  133.83  134.83   22648  134.653   \n",
       "\n",
       "   barCount  \n",
       "0     17523  \n",
       "1     26974  \n",
       "2     17853  \n",
       "3     13497  \n",
       "4     12602  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"df.head(5)\";\n",
       "                var nbb_formatted_code = \"df.head(5)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"df = df[[\\\"close\\\"]].copy()\";\n",
       "                var nbb_formatted_code = \"df = df[[\\\"close\\\"]].copy()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[[\"close\"]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1753.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>164.330610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.125225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>132.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>143.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>159.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>183.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>216.540000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close\n",
       "count  1753.000000\n",
       "mean    164.330610\n",
       "std      23.125225\n",
       "min     132.670000\n",
       "25%     143.320000\n",
       "50%     159.750000\n",
       "75%     183.390000\n",
       "max     216.540000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"df.describe()\";\n",
       "                var nbb_formatted_code = \"df.describe()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An ARIMA model is characterized by 3 terms (p, d, q):\n",
    "\n",
    "- p is the order of the AR term\n",
    "\n",
    "- d is the number of differencing required to make the time series stationary\n",
    "\n",
    "- q is the order of the MA term\n",
    "\n",
    "As we see in the parameters required by the model, any stationary time series can be modeled with ARIMA models.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stationarity\n",
    "\n",
    "Subtract the previous value from the current value. Now if we just difference once, we might not get a stationary series so we might need to do that multiple times. \n",
    "\n",
    "And the minimum number of differencing operations needed to make the series stationary needs to be imputed into our ARIMA model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ADF test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the Augumented Dickey Fuller (ADF) test to check if the price series is stationary.\n",
    "\n",
    "The null hypothesis of the ADF test is that the time series is non-stationary. So, if the p-value of the test is less than the significance level (0.05) then we can reject the null hypothesis and infer that the time series is indeed stationary.\n",
    "\n",
    "So, in our case, if the p-value > 0.05 we'll need to find the order of differencing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -0.06770371326952478\n",
      "p-value: 0.9525618600853154\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"# Check if price series is stationary\\nfrom statsmodels.tsa.stattools import adfuller\\n\\nresult = adfuller(df.close.dropna())\\nprint(f\\\"ADF Statistic: {result[0]}\\\")\\nprint(f\\\"p-value: {result[1]}\\\")\";\n",
       "                var nbb_formatted_code = \"# Check if price series is stationary\\nfrom statsmodels.tsa.stattools import adfuller\\n\\nresult = adfuller(df.close.dropna())\\nprint(f\\\"ADF Statistic: {result[0]}\\\")\\nprint(f\\\"p-value: {result[1]}\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check if price series is stationary\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "result = adfuller(df.close.dropna())\n",
    "print(f\"ADF Statistic: {result[0]}\")\n",
    "print(f\"p-value: {result[1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Autocorrelation Function (ACF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"from statsmodels.graphics.tsaplots import plot_acf\";\n",
       "                var nbb_formatted_code = \"from statsmodels.graphics.tsaplots import plot_acf\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(df.close)\\nax1.set_title(\\\"Original\\\")\\n# add ; at the end of the plot function so that the plot is not duplicated\\nplot_acf(df.close, ax=ax2);\";\n",
       "                var nbb_formatted_code = \"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(df.close)\\nax1.set_title(\\\"Original\\\")\\n# add ; at the end of the plot function so that the plot is not duplicated\\nplot_acf(df.close, ax=ax2)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "ax1.plot(df.close)\n",
    "ax1.set_title(\"Original\")\n",
    "# add ; at the end of the plot function so that the plot is not duplicated\n",
    "plot_acf(df.close, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"diff = df.close.diff().dropna()\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(diff)\\nax1.set_title(\\\"Difference once\\\")\\nplot_acf(diff, ax=ax2)\";\n",
       "                var nbb_formatted_code = \"diff = df.close.diff().dropna()\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(diff)\\nax1.set_title(\\\"Difference once\\\")\\nplot_acf(diff, ax=ax2)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = df.close.diff().dropna()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "ax1.plot(diff)\n",
    "ax1.set_title(\"Difference once\")\n",
    "plot_acf(diff, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"diff = df.close.diff().diff().dropna()\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(diff)\\nax1.set_title(\\\"Difference twice\\\")\\nplot_acf(diff, ax=ax2);\";\n",
       "                var nbb_formatted_code = \"diff = df.close.diff().diff().dropna()\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(diff)\\nax1.set_title(\\\"Difference twice\\\")\\nplot_acf(diff, ax=ax2)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = df.close.diff().diff().dropna()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "ax1.plot(diff)\n",
    "ax1.set_title(\"Difference twice\")\n",
    "plot_acf(diff, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the pmdarima package to get the number of differencing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"#!pipenv install --skip-lock pmdarima\\n\\nfrom pmdarima.arima.utils import ndiffs\";\n",
       "                var nbb_formatted_code = \"#!pipenv install --skip-lock pmdarima\\n\\nfrom pmdarima.arima.utils import ndiffs\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!pipenv install --skip-lock pmdarima\n",
    "\n",
    "from pmdarima.arima.utils import ndiffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"ndiffs(df.close, test=\\\"adf\\\")\";\n",
       "                var nbb_formatted_code = \"ndiffs(df.close, test=\\\"adf\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndiffs(df.close, test=\"adf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### p\n",
    "\n",
    "p is the order of the Auto Regressive (AR) term. It refers to the number of lags to be used as predictors. \n",
    "\n",
    "We can find out the required number of AR terms by inspecting the Partial Autocorrelation (PACF) plot.\n",
    "\n",
    "The partial autocorrelation represents the correlation between the series and its lags. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"from statsmodels.graphics.tsaplots import plot_pacf\";\n",
       "                var nbb_formatted_code = \"from statsmodels.graphics.tsaplots import plot_pacf\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"diff = df.close.diff().dropna()\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(diff)\\nax1.set_title(\\\"Difference once\\\")\\nax2.set_ylim(0, 1)\\nplot_pacf(diff, ax=ax2);\";\n",
       "                var nbb_formatted_code = \"diff = df.close.diff().dropna()\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(diff)\\nax1.set_title(\\\"Difference once\\\")\\nax2.set_ylim(0, 1)\\nplot_pacf(diff, ax=ax2)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = df.close.diff().dropna()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "ax1.plot(diff)\n",
    "ax1.set_title(\"Difference once\")\n",
    "ax2.set_ylim(0, 1)\n",
    "plot_pacf(diff, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the PACF lag 6 is significant as it's above the significance line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### q\n",
    "\n",
    "q is the order of the Moving Average (MA) term. It refers to the number of lagged forecast errors that should go into the ARIMA Model.\n",
    "\n",
    "We can look at the ACF plot for the number of MA terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"diff = df.close.diff().dropna()\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(diff)\\nax1.set_title(\\\"Difference once\\\")\\nax2.set_ylim(0, 1)\\nplot_acf(diff, ax=ax2);\";\n",
       "                var nbb_formatted_code = \"diff = df.close.diff().dropna()\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(diff)\\nax1.set_title(\\\"Difference once\\\")\\nax2.set_ylim(0, 1)\\nplot_acf(diff, ax=ax2)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = df.close.diff().dropna()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "ax1.plot(diff)\n",
    "ax1.set_title(\"Difference once\")\n",
    "ax2.set_ylim(0, 1)\n",
    "plot_acf(diff, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting the ARIMA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"from statsmodels.tsa.arima_model import ARIMA\\n\\n# ARIMA Model\\nmodel = ARIMA(df.close, order=(6, 1, 3))\\nresult = model.fit(disp=0)\";\n",
       "                var nbb_formatted_code = \"from statsmodels.tsa.arima_model import ARIMA\\n\\n# ARIMA Model\\nmodel = ARIMA(df.close, order=(6, 1, 3))\\nresult = model.fit(disp=0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "# ARIMA Model\n",
    "model = ARIMA(df.close, order=(6, 1, 3))\n",
    "result = model.fit(disp=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                D.close   No. Observations:                 1752\n",
      "Model:                 ARIMA(6, 1, 3)   Log Likelihood               -2984.206\n",
      "Method:                       css-mle   S.D. of innovations              1.329\n",
      "Date:                Mon, 12 Oct 2020   AIC                           5990.412\n",
      "Time:                        08:38:06   BIC                           6050.566\n",
      "Sample:                             1   HQIC                          6012.647\n",
      "                                                                              \n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const             0.0461      0.031      1.480      0.139      -0.015       0.107\n",
      "ar.L1.D.close    -0.6365      0.119     -5.346      0.000      -0.870      -0.403\n",
      "ar.L2.D.close    -0.4278      0.134     -3.202      0.001      -0.690      -0.166\n",
      "ar.L3.D.close    -0.4730      0.112     -4.236      0.000      -0.692      -0.254\n",
      "ar.L4.D.close    -0.0086      0.036     -0.240      0.810      -0.079       0.062\n",
      "ar.L5.D.close     0.0711      0.028      2.506      0.012       0.015       0.127\n",
      "ar.L6.D.close     0.1765      0.024      7.368      0.000       0.130       0.223\n",
      "ma.L1.D.close     0.4605      0.120      3.824      0.000       0.224       0.697\n",
      "ma.L2.D.close     0.3270      0.122      2.682      0.007       0.088       0.566\n",
      "ma.L3.D.close     0.4702      0.099      4.758      0.000       0.276       0.664\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -1.1009           -0.0000j            1.1009           -0.5000\n",
      "AR.2            0.3084           -1.1242j            1.1657           -0.2074\n",
      "AR.3            0.3084           +1.1242j            1.1657            0.2074\n",
      "AR.4           -0.8271           -1.2237j            1.4770           -0.3446\n",
      "AR.5           -0.8271           +1.2237j            1.4770            0.3446\n",
      "AR.6            1.7356           -0.0000j            1.7356           -0.0000\n",
      "MA.1           -1.2592           -0.0000j            1.2592           -0.5000\n",
      "MA.2            0.2818           -1.2688j            1.2997           -0.2152\n",
      "MA.3            0.2818           +1.2688j            1.2997            0.2152\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"print(result.summary())\";\n",
       "                var nbb_formatted_code = \"print(result.summary())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.23256910e-04, 0.00000000e+00, 1.11628455e-03, 7.14422112e-03,\n",
       "        7.05491836e-02, 2.95368892e-01, 1.38419284e-02, 2.45582601e-03,\n",
       "        2.23256910e-04, 2.23256910e-04]),\n",
       " array([-13.36180137, -10.80521194,  -8.24862251,  -5.69203308,\n",
       "         -3.13544366,  -0.57885423,   1.9777352 ,   4.53432463,\n",
       "          7.09091405,   9.64750348,  12.20409291]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"# Plot residual errors\\nresiduals = pd.DataFrame(result.resid)\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(residuals)\\nax2.hist(residuals, density=True)\";\n",
       "                var nbb_formatted_code = \"# Plot residual errors\\nresiduals = pd.DataFrame(result.resid)\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\\n\\nax1.plot(residuals)\\nax2.hist(residuals, density=True)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residual errors\n",
    "residuals = pd.DataFrame(result.resid)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "ax1.plot(residuals)\n",
    "ax2.hist(residuals, density=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD3CAYAAADi8sSvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABhEUlEQVR4nO29d3xb53X//77YkxvcFClR1NWWbNmWbHnGTpzhOImdnWY5btKspm1a99u0aZo2P8dpRpvW2ctJHKeJ4zhxdmwnnvKUtccVRYmkuBcAYs/7++MCIEGAEkmRBEA979fLLwt34D4PL/DBueec5xxJVVUEAoFAsDLQFXoAAoFAIFg8hKgLBALBCkKIukAgEKwghKgLBALBCkKIukAgEKwgDIW8+Oiob06pN5WVNtzu4FIPZ9lYSfNZSXOBlTWflTQXEPOZjsvllGbbVxKWusGgL/QQFpWVNJ+VNBdYWfNZSXMBMZ+5UhKiLhAIBIK5IURdIBAIVhBC1AUCgWAFMadAqSzLO4HPKYpyrSzLG4FvAhLQCdyuKEpcluV/BN4GTAL/qSjKr5dq0AKBQCDIzzktdVmW7wC+DVhSm+4EPqEoyu7U69fKsrwFeDuwC3gF8O+yLNuWYLwCgUAgOAtzsdS7gFuAH6Ze36ooSkKWZRNQD3iBDcBjiqKEAWRZ7gS2As+e7Y0rK21zjgC7XM45HVcqrKT5rKS5wMqaz0qaC4j5zIVzirqiKA/Istw27XVCluVW4BE0QT8A1AL/JMuyEzABV6C5aM7KXHM0XS4no6O+OR1bCqyk+aykucDKms9KmguI+cw8dzYWFChVFKVHUZQO4OvAlxRFOQbcDfw+9f/ngLGFvLdgdk57e3nszNOFHoZAIChi5i3qsiw/JMtyR+qlD0jKsuwCnCk/+18BLcDhxRumAODBk7/m/s5f4g57Cj0UgUBQpCykTMBdwD2yLEeBIHA7mlW+QZblF4Ao8A+KoiQWb5iCWCJGz+QZAMbDbiotFYUdkGBOuH0RKhwmJGnWVd0CwaIyJ1FXFKUbLbMFRVH2ALvzHPaBxRuWYCY9vj7iqvY7ORF2A6sBGPOGeOHYCDdetgqdTghHMfHYvn5+8AeF99+8kV0b6ws9HMEFglh8VCJ0eU5n/j2Rcr+oqsp3f3OM+x/r4mjPRIFGJsjHwFiAHz/aCcCzR4YLPBrBhYQQ9RKhy9ud+fdEWBPww6cnON7rAaBvJFCAUQnyEU8k+eavjhCLJ7GZDRztniAUiRd6WIILBCHqJUBSTXLK202FuRzQLPWkqvKzx7oyx/SP+Qs1PMEMHnziFL3Dfq7a2sANlzQTT6gcOjVe6GEJLhCEqJcAg4FhQvEw6ys7cBjtTITdPHd0mDMjfnZtrMOgl+gfFZZ6MXC8x83vn+ultsLK227o4OJ1LgBeOjFa4JEJLhSEqJcAaX96e8VqqiwVTITd/PyJLgx6iVuuXkNDtZ2BsQBJdU49RwRLRCAc41u/PookSfzlzRuxmAy01DqoKbdwsGucWDxZ6CEKLgCEqJcAJzOi3kaVpZJYMs54YJLrLmqmpsJKk8tONJ5kzBMq8EgvXFRV5Yd/UHD7Ity8u432Rs1VJkkSF69zEY4mOCaC2YJlQIh6gYnFE/zXTw/wy6dO592vqipd3m6cRge11hrKjBUAWBxRbrqiFYCmGjuAcMEUkOeODfP8sRHam8q4ckclLw7v5/+UB/nffd9idZtW30i4YATLQUF7lArggcdPcejUOMd63Fy/oxmH1Zi1fyLsxhPxst21GUmSGB7SXCzbN9pw2kwANLkcAPSNBbgo5cMVTDERdlNprliyBUD+UIz7HjuIebVCuDnAJ5/5adb+jVUyZTYj+zrHeNeNqlhPIFhShKVeQI71uPnjC2fQ6yTiiSR7Dg3mHJNOZWwvb8MbiHLsZBiAluap3+PmjKUuMmBm0jN5hk/u+Sx7Bp9fsmvc/9gJYs0voHOdIRgPsql6Pa9vfzVv6ngdAMFEiO0dLnzBGCf7vUs2DoEAhKgXjGA4znd+cxSdJPHRW7di0Ov48/4B1BnBzulB0l8/3U00YAZgMjYlDlXlFswmPf1jwv0yk+Gg5vLYP7I0pYhOnPGwZ+g5dA4vF7m28vmr/40PbbuNl7dey7rKdgACsaDIghEsG0LUC8SPHj7BxGSEm65oZWt7NZeur2V4IphZTJTmpLcbk96EOVHJY/v7qbFWAlMLkAB0kkRTjZ2h8SDxhMiwmE44rj3ZdHpOEUsu7gKgeCLJ9x7di7H5BBa9lbfIr0cnTX2l7EatT0wgFmBDayUWk56XTozm/HALBIuJEPUC8PTBAZ45MkRbvZObrmgD4LqLmgCtXkgafyzAUGCYNWWt/OqpXhJJlTdcKWPRWzKlAtI01dhJJFWGJ+ZWo/5CIZQS9VgyxilP97zPP9nn5dkjQySSuT+Wv3+uB3f5C0j6JG+VX4/T5Mjanxb1YCyE0aBja3s1Y94wZ0aEm0ywdAhRX2Y8/ghfuf8AJoOOv3ztRgx67Ra0N5XR7LLz0olRvP4IQEaEaoyNPHtkiJZaB5dtqKPaWslE2J1l8aWDpcIFk01a1AGOTZyY17nJpMrdDx7im786yr999wUOn55aFTriCfFr5Un05RNsqJS5pG57zvkGnQGz3kQgpt0T4YIRLAdC1JcRVVW553fH8QWjvOm6tTRU2zP7JEni2ouaSCRVnjyoBUzTQdLuk0ZU4NZr1qCTJKosFYQTEYLxqbz0JpdIa8xHKDEl6sfdnfM692S/l8lAlOoyMwNjAb70kwP89/0HGBgLcM/D+9A1HccomfiLjW+cNbPGZrDhj2lPT1vWVGPQ64SoC5YUIerLyOnBSQ4NdLOlo5LrLm7K2X/5pnrMRj2P7x8gmVTp8pxGh47OE9DRXM6WNdUAVFnSfnV35txMBoyw1LMIxbQfvlprDWd8/fiis7s+9g7v56WBQ5nXafF9540yn3rvpaxfVcHBrnE++e1nOaV7GskQ543rbsrU5MmHw2gjENdE3Wo2sLGtkr7RACNzbOUoWHnEknEe6X2ckcDS1AMSor4MqKrKsYkTfEf5DpYtTxNofJJoIppznNVsYOfGWsYnw+zrGqLX148hWgFJA7de056xBvOJepndhMNqFGmNM0hb6hfVbgVAcZ/Me9xIcJTvHrmPu578Kvcd/xmReISXToxiMenZ0FrFqjon//C2i/jIGzZTvnoAfeUobY42djfuPOv17UY70UQ0E6SdcsGIbo8XKr89/TAPnvwNe/sPLsn7C1FfQlRV5cj4cb649yvcvf/beNQhkmEbg6E+vnHwHqKJWM4516YCpg8fOUxCTRAYL2NrezXrWioyx6RFfXyaqEupDJgRd4hoTDSdShOOh9FJOra5NgGz+9Wf7H8WgApLGU8PPM+dz/0P49FRtrZXYzRoX5NOTxd/nPwxEdchzHoz79n8lnMuaJoKlmqWubyqAhBrCi5Uzvj6eaT3caoslVy3+vIluYZYUbpE+KJ+vnbwe5kWdJurNrL3yXJWVzTTvLOT5/v2861DP+D9W9+NUTd1G9rqy2ird3Jqcj/GMkj6K7nl9Wuy3rs6j6UOml9dOeNhcDxIa/3s3cYvJELxMFa9hRZnE3ajjeMTnaiqmiXG0USUZwZfxGly8OVXf5rvPf8zHut7GvPGcRxVNgYDLn5x8rccHj8GwKV1F/HaNa+kOpVeejam0hqDlJvLqHJq6wwmfJElmK2gmEkkE9x77H6SapK3y7diMVrwkWvYnS/CUl8iHu/bQ8/kGTZXb+ATl/0tl1peTTJQzpY1NfzNrvexqXo9RycUvnfkPhLJKcs6qSZZvymBvkoLll7UsI5VddkCPeV+8WRtz5QLEFZghlA8jMVgQSfpkCvX4ol4GQ6OZB3zwvA+QvEQVzbuxGq08KZ1r8MxfDmoep7xPsxnnvsih8eP0VGxhjsu+Sjv2fS2OQk6gG1arjqA0aDHYTXiFqJ+wfFw7+P0+QfY1XAJG6rXLdl1hKW+BCTVJM8P7cWkN/HeTW/HYjDz8GnNytu8uhqD3sDtm9/J1w5+jwOjh/nBsZ9wY+vLeH7oJV4Y3ocn4kVng6S7jje+bEPO+zuMdow6IxOh7Kp/TSJYmkMoHsJlrQFgQ9U6Xho5yLGJTurtdYDmInui7xl0ko4rm3YBMDwRZLSnnI2m12BcfYhAPMhr19zI5uoN864fM91ST1PlNDPsDuU8MQhWLkOBEX53+mHKTE5uXXvTkl5LiPoS0OXpZjzsZmf9DiwGM6qqcujUBHaLgbaUW8SkN/KBLe/mKwe+zYvD+3lxeD8AVoOF3Y07qaODyuZ66iptOe8vSRJVlsocS71RVGvMIqkmiSSiWA0WANZXdQBwfOIE17VcCcDpyR76/ANsd23JZLGks152rm3jqm1XnNcY7IaUqMenRL3SaaZ3xE8oEsdmMc52qmCFkFST/Oj4/cTVBG+V34BFb2Vf5yhXOCxLcj0h6kvAc0N7AdjVsAOAgfEgbl+EyzbUZlXosxjMfGjbbXzvyI+RgMvqd7ClZiMm/bm/6NWWSoaDI4RT7gUAh9VIhcMkWtulSJcIsBqsgOa2qrO5OOE5RTwZx6Az8HjfHgCuaZ4KWr10YhRJgu0dNec9hnyWemWZdr8mfBEh6hcAj/ft4ZS3h4trt7KubD3/88BBDnaNE0nArvWLX1VViPoiE01E2TdykEpzBWsrtADnkVR/ys2rq3OOtxqsfGjbbfO+TpWlAtD86o2O+sz2JpeDI6cnCIbj2CwX9u0NZUR9yiJaX9XB4317OO3toc5ey76RQ9Tb6+io0IpvjXtDdA1Msn5VRaa08fmQV9RTwVK3L0Kzy5H3PMHKYDw0wUNdv8NusPGyuhu584d76R8LsHl1Fddf2kLAFz73m8yTOX3rZVneCXxOUZRrZVneCHwTkIBO4HZFUeKyLH8ceDuQBO5UFOXBRR9tCbB/9DDhRIRrm3dnijsdOq35vjetrlq060zPVc8S9Ro7R05PMDAWYG3z7ItiLgSCKVG3TBP1DVXreLxvD8cnOunydpNQE1zddHnGt/3s4SGARatLP5tPHRDB0guA+zt/STQZ45qaG/mv+47jD8W44ZJm3vKytdgsxsKIuizLdwDvBNKO2juBTyiK8oQsy/cAr5Vl+c/Ax4C1gB3YD1yQov780EsAXJZyvURjCU6c8dDscmQstMXgbGmNAH1j/vMWdVVVGQ9P0Ok+RafnFBaDmTd1vK5kgnvhVBmF6ZZ6R8UadJKOPyovoTfFMBtMXFZ/cWb/s6ma9jsWTdS1+xHMY6lPTC7+F1pQPBwaO8qhsWPUGpr5zW/jgMS7Xilz7fbc1eSLyVws9S7gFuCHqde3KoqSkGXZBNQDXjTB70ETdDuatX7B4Yl4OT7RyeqyVupsmigoZzzE4kk2r1k8Kx2gypq7AAnIPM6fT7D0+EQnzw6+SKfnFJ5IdlOHDVXr2FKzccHvvZzkc79YDBYarc30qb0kAcP4avqHIqxttuAPxTjUNUZbvZOqssUJYlkNFiSkTP0XIPPeIld95RJNxLj/xENI6Oh9qQ2bycCH37CF9a1zS4U9H84p6oqiPCDLctu01wlZlluBR9AE/UBq1xngKKAHPjuXi1dW2jAY9HMaqMtV/Itpnj62BxWV6zuuyIy3a083AFde1Jw1h/Odj97eAnshoPqz3stZpgUFR73hBV0jkUzw3SfvJRAL4TQ72Nl8ERtdHVTbKvnC09/g0b7HuG79ZVnW+vTr+EMxnj00yDUXN2Gc471dKowBbYy1FRVZY1xT3k5fsBeAYF8jd53cy1tfLuOqtJJIqlw1416dLw6TjYg6dT/S9ygQji/557oUvjfzoVTm89PDv2I8PIF+fC3GRDlf+Luraa7NHftSzGdBkTRFUXqADlmWbwe+BDwANACrU4f8QZblpxVFOWsPMfccixq5XE5GR30LGeqyoaoqfzq5B4POgGyTM+N94cgQJqMOl8OU2bYY80mqEnpJz6B3NOe9XBUWTg94F3SNLk83gViIKxou5e3rp6oPun0R2u3r6Jw4wdMn9iNXrc2Zi8cf4Ys/2U//aIBgMMLlm+pnvc5yMOzWnmLiYSnrb+GINABQo2/iHbdcw7d+dYT7/qigT2UmrW8uW9TPm1VvxRv2Zb2n3WLQ8uGX8HNdCt+b+VAq8xkJjvGLY3/EhB3v6TZev3sVZonc7+l5zOdsPwbzXlEqy/JDsix3pF760FwtbiAERBRFCQMeoGK+713K9Pr6GAqOsKVmY2YV4bg3zOB4kPWrKjP1QxYLnaSj0lLBeHgiZ19TjQNfMMZkILdo2Lk4OqEAsKVmY5Y1/s2HjnDkeS1757enH8k5b8QT4rP37s24fYohVz4US7lf9NmuFHO8muipzVxV9UrWtVTw6dsu47INtSSSKi11zqySyIuB3WgjEAtm1b+vdJpxL0GQTFBYVFXl/s5fEk/GCXZ1UON08Kqdq5Z1DAtRmruAe1LB0XehBU2fBF4AnpVl+RngBPDw4g2z+Ennpu+cFnRLN1VIl8xdbKoslfiifmIzCoOlg6XdQ5Pzfs+j4wp6SZ/prwkQCMc40edBDZST8FZz0nuKp08dzezvG/Hz2Xv3MuoJc/3FzQAMjheBqCe0QOn07BfQ3B6JsWYanFrcw2Yx8oGbN/Hxt27nE++5dNHHYTfaUguhpnzolU4LoUiCUGRxW+wJCsuBsSMcHVewRuuJjdfx1us7lt0NOSf3i6Io3cCu1L/3ALvzHPMp4FOLObhSIZ6M8+LwfpxGBxur5Mz2w6c0K3rzIqYyTieTARPxZAKzAB3NFUAPX/3FYV63ezUvv7Ql02HpbPiifnp9fayraM8SwiOnJ1BVeO0VbYwlDOxXf829+3/L5IidSzc38rn7XiIQjvO26zu44ZJmnj06xMB44euFh/MESkHz+wM4rVMLfyRJYlNb1ZI84qczYAKxYObvOj1X3Wq+sNcTrBQiiSg/O/EQOnS4j69l0+pqLlqEBWzzRRT0WgSOjB8nEAtyaf1F6HXar3IimeRoj5uacgu1ldYluW5mAVIoOwNGbnVw/fVgMqnc/1gXn77nBTr7POd8v3RZ2o3Vctb29I/T9o4abr/2KurNTegqRvjZc/u54+4nCUUSvO81G3j5pS1IkkRDtZ1Rd6jgTbBDM1aUpgmkRN1hXZ7VnDZjKjAqctVXLP5YgO8cvhd3xIN+vB1d1Mnbb+goSPqvEPVF4KTnNADbXJsz204P+AhF4mxZU71kNzZfswxVVblPeYA9vt/zuteauWZ7I/2jAT5770vc87tjZ33cPzquifqGqqkKcqqqcuj0OE6bkdZ6J5Ik8Qb5FQC41vVhMen58C2b2b2lIXNOQ7WNpKoy7A5RSPKlNAL4UqJuXyZRtxumLPU0mVx14VcveTrdp/js8//NkfHjVEvNTJ5u5YZLmhc9NjNXhKgvAoOBYQCaHFPCNpDyKa9uKFuy6+ZbgPTM4AuZ4mDu2BjvfuV6PvHOHTS77DxxYJDfPNOT972SapJjEwplJmfWPM6M+PH6o2xeXYUu9eO0qXo9zY5G/OZevvSPl3BRR/ZCnfSHebDA1SJD8TB6SZ9Vrx4094vJoMNsXB5fZ2ZV6fSiXmXCUi91kmqS355+mC/v+waTUR83NF3PyN4tlFlt3Lx79bnfYIkQor4IDPiHqDRXZFmEY17NAqspX5pKbJDbAWnAP8RPT/wSSyrbYySotUxb21TOJ965Awno6vfmfa8+3wD+WICNVXLWk8WhU7nBXkmSuLHtZaio/PpEbjy8sUYTsUIHS0PxsLb4Z8aTUiAUWzYrHWar/5JagDQpRL0UGQmO8r/7vsVvTj9Mhbmcv7nor/B3txGNq7zxmvaCxklEhOY8CcaCeKOTWQFS0ApDAVQvoahXmMuRkJgIu4kmonznyI+IJWO8Z8vb+PHxBxgJTnWtt5gM1Ffb6B3x563jfTTjT88u3n/o1AQSuXVrtrs2U2dz8UT3s7y88XrKzVN5sxlLvcDB0nA8lON6Ac39UlexNHGOfNhnNMoA4VMvNZJqkp7JPg6OHeHQ2NHM0/m2mk28Y8ObMGDm2aNPUek0c8Xmwq7PEKJ+ngykbm6Doy5r+7g3jCSxqPVeZqLX6akwlzMR9nD/iYcYCgxzTfMVbHdt5tHeJ+ie7M2UmAVYVedkcHyYUW+Y2hmidnRcQUJCrurIbAuG43T1e2lrKMupWKiTdFxWv4Nfnfo93ZO9mR6goP2QGQ26jAuqUITiYcrN2e6vWDxJJJoouKVuNRuwmvUiV70E+POZp/hjz5+ZjGpZUUadgS01G9hRu51L6rYjSRJPHxokFElw/Y6WrPLahUCI+nmS/sVusGf/Oo9PhqlwmOeUSng+VFkq6fKeZs/g87Q4GnlD+2sAqLXVcMrbzXhogjp7LQCr6hw8d3SY3iFflqiH4iFOT/bQVtaCwzgV3DnWM0EiqbJllro1zSnfe59/IEvUdZJEfZWNofEgSVXN+OKXk0QyQTQZwzIj88W/zJkvkF/UQXPBCEu9+Hm45zFC8RC7Gi5ha81G1letw6zPNnKePDAAwJVbG/K9xbIifOrnSVrUG+1TlnoimcTtiy6p6yVN2q9u1pu4bfM7MKYabNRZteDlSGgsc2y612nPcHYetjJxkqSaZMOMVMZDqVTG2RZPNTsbAej3D+bsa6i2EY0nmfAWxhKdLfMlk85oW05RT2W/xGeKuplAOE4kmsh3mqAISCQTTEZ9rHK28M4Nb2aba3OOoA9NBDnR52VDa2XOE3AhEKJ+ngz6tfrb9dNE3e2LkFTVJQ2Spml2apbB2+RbqZ22AKnWpi16SAdLAVpTot47nN0ZKV0aYHpcQGvBN47dYpg1g6fcVIbT7KDfN5CzrzHtV58ojF89I+r6/AuPHMvYccikM2LQGfJY6im/ul9Y68XKZNSHikqFefYstrSVftW2wlvpINwv581gYJgaS1XWr/d4yjqtXqTyrWfjuuYr2VazGZct25pOC/z0YKnDaqS6zEzvNEtdVVWOjp/AbrDRWtac2d4/Fsjbgm86kiTRVtHMoeHjmUyTNA01U2mNS1Um4WykSwRYjbOI+jJa6pIkYTfYckQ9EyydDFNflduLVlB40qWnKyz5exPEE0mePjyE3WJYtBr854uw1M8DX9SPL+bPDZKmmh8sh/tFr9PnCDpAjbUaCSnLUgfNBeMNRPGkrMOh4AjuiIf1VR2ZTk0wtYr0XILcWqH9EMx0wTRUayJVqHIB4XNZ6svoU4epol7TmVqAJCz1YsUT0eonpZuSz+Rg1ziTgSi7NtUXvNR0GiHq58FsQdJMjvoyWOqzYdIbqbRUZPnUYcqvnrbWj41rrpdcf3qqr+o5RL1tFlGvq7QhSYXLVQ+eo+5LIUQ9FA+RVKdKJ6Rz1UWwtHjJWOqziHrG9VIEAdI0QtTPg4GA5k9vsOemM8LyWOpno9ZagyfiJRyfEo3WTLBU86tn8tOnlQYIR+OcOOOhtc5Juf3szZfTot43w69uNOhwVVgLlqseyvQnLXz2C0xlwARjU6UTqoSlXvScTdTdvggHT43TVu/MGEvFgBD18+Bs6YzAorVEWyjpYOloaDyzbVWd1u6ud8hHNBGj03OKJkdDVj73sR43iaQ6pxZ8jWX1GCR93gyYxmo7/lCMyeD867qfL+eq0FgoUZ++AClTKkD0Ki1apkQ9N1D69KFBVBWu2ta43MM6K0LUz4NB/xASEvW27ADJuDeM02Zcttois5EvWFrpNOOwGukZ9tHr6yOejGfVToe5+9MBDDo99fY6BgKDJJLZqXlpv3ohasCE8jSdhsKJus2QW//FZjZgMuqE+6WI8US8SEiUm7JFPamqPHlwAJNBx84NdbOcXRiEqC8QVVUZDAzjslVncsNBu9njk5FlyXw5F/nSGiVJorXOwZg3zPGxLgDWlLdl9idVlQNdY1jNBtqb5laMrNnRSCwZZ3SG//5c5QKG3cElaxIxW566PxRDr5OwmJb3BzffAiRJkqh0WoT7pYjxhL04TY5MSe00Sq+HUU+YS9bXYrMUVxKhEPUFMhn1EYyHaJzhevEFosQTyWXJUT8XtZkFSKNZ21fVa/6/42OnAFhT3prZp/S4mZiMsGOdC71ubh+PJmd6ZemMDJhMYa9cUR9xB/nkt5/jW786mrNvMTibqDusxmWvcz29UcZ0qpxm/KEYsbhYgFRsqKqKJzqZ15++57D2WS+mAGkaIeoLZLYg6ViRBElBa6Khl/Q5aY1asFSlP9hHtaUy60P71CHtwzqf5c7NDs2nODNY2lCVttRz3S9/eqmfeEJl/8kx+kb9OfvPl9kaZPiDsWXNUU8zW6kAUdireAnEg8ST8byi3jcawKDX0dFSsfwDOwdC1BfIVJB0lhz1InC/6HV6aqzVjObJVZcsAaKEs1wvwXCcvcootRVWOprzp3DlI10DZmaw1GYxUO4w5Yh6JJrgqYODGPSatfy7Z3vnM605Ec5kv0zdh0QySTASX9bVpGlmrf8i6qoXLZ7w7EHSUXcIV4WlIHWNzoUQ9QWSLg+Qk/lSRJY6aH71QDyIf1rWRW2lFVOFtqhiuuvlRWWEaDzJ7i3183JP2Iw2Ks0V9PvzlwsYn4wQjk75zp85MkQwEufVu1pprLHz/LHhzN9tsQjFwxh1hqwGGYGwNoblDpJC/uwXmFZXXYh60TFbOmMgHCMYieMqgjov+RCivkAGA8PoJF0mGJlmrIgsdcgfLNVJEo5qbfFRs31VZvtTBweRIKs13VxpdjbgjfrwRbNdKekMmKFUDRhVVXl0bx96ncS1FzXxqp2rSCRV/vDC4lrroXgoy0oHzfUCy1siIM1U96PsFn+Vwv1StMwm6qMe7R4WQ/GufAhRXwDpzJc6mytTqzzN+DJ0PJoPmWqNwexgqWqbQE3oSQSn/N4n+71sbKtcUH59U9qvPsNan2ptp4n68V4P/WMBdsguKhxmdm6so9Jp5okDA5l0w8VgZi0aKFw6I4DdcA6fuuiAVHTMViJgJNV7V1jqKwh3xEM4Ecnxp4PmU7ea9dgK4LfNhyuPpe6PBghJXpL+CvpGNJHZc1hzJy3ESoep/qwzg6WNmRowmtvhT3v7ALhhRwsABr2OGy9tIRpL8qeX+hZ07XyEEmGs+uJYTQpafMOit+Rxv4gG1MXKbAuP0pa6EPUVxFQN9Wx/uqqqjHvDReN6genulylL/fSk1nw66aukd9hHMqmy5/AQVrOBixdYaS6dAZNT2CtVrXFoPMi4N8xLnSM0N+kY1Z3gx8cf4PhEJ1dvb8RuMfDIi31EYuef2hdLxokn40VlqQPYjdYcS91hNWLQ64RPvQhJi3r5LO4XV2VxivqcsuZlWd4JfE5RlGtlWd4IfBOQgE7gdmAz8N/TTtkFvF5RlN8v7nCLgwF//nTGQDhOOJqgprx4bna5qQyT3pRV2KvL0w2AFNRE/Uj3BG5fhGu3N2Ja4CrYGqtWfnimqJfbTVjNOrojx/nK3mcxbT3DuDnMvce1/XtHDvIvO/+O6y5u4td7enjq4CDX72jOc4W5M1uJgHSDjOVsZTcdu9HGYGAka5skSVQ5zcKnXoR4I5NYDRYshuyWlKOe4nKxzuSclrosy3cA3wbSM7gT+ISiKLtTr1+rKMp+RVGuVRTlWuArwAMrVdDhLOmMy1hHfa5IkkSdtYaR4FimQuApb7dW3sDSxJmRAE/s11wmC3W9gNaztNHewFBwhFhiyjcuSRLOtjME615gmE50+iRbazbxxo6bec3qlxOKh/jR8Z9x/cXNGA06/vB8L4lk8ixXOjfBWUoE+FKi7iyYqNuJJWNEE9mxg0qnmcnUojVB8eCOeHOsdNAs9XKHqeBlQGZjLu6XLuCWaa9vVRTlCVmWTUA94E3vkGXZDnwa+NiijrLIGAwMYdAZqLFm10ZZzjrq86HW5iKWjOGNTBJPxunx9dHkaKCttpJ4IsneE6M0VNtY0zi3sgCz0exsJKkmGQwOZ7b1+vrwlx1BjZoJH7qC60zv4QNb3811LVfyqrYb2FC1jqPjCocn93PllgbGvGFeOD5ylqucm3w56lAM7pdUpcaZbe1SueoeYa0XDZFElFA8ROUMUY8nkoxP5jZuLybO6X5RFOUBWZbbpr1OyLLcCjyCJugHph3+PuB+RVGyV7vMQmWlDcMcC8u7XMVR2jKpJhkKjtBUVk99XUXWvsgxTYxWt1Scc7zLOZ+2mib2jhwgYgqAPkY8GWdTfQf15TU8eVBzl9y4q43a2oWJenou672rebL/GSZx43JtIBKP8sMXfoIqJYme2oIULuPWG2RclVNdfj66+938/e8/w89P/pp/vPbjPL6/nx8/chKT2cTLL1u1oM7sgwntnJry8qy/cyyhan+PlkocttlLCi/VvalxVsAwmBzgqpi6RnNdGRwZJqnXL/q1i+V7s1gs13wGfJphUldenXXNgTE/qgot9WWLMpalmM+CKtEoitIDdMiyfDvwJeDdqV3vAN441/dxu+dWa9vlcjI66jv3gcvAaHCcaCJGrdmVM6aeAe2hxQhnHe9yz8eBJtYnBnqJJrUyuI2mRipSLfgkCba0VS5oTNPnUo5WqvfY4Ck2O7fwY+XnDPiG2ezcwQuTNexY50KKJ2Zcx8gb197MD479hB8f+QnvePlr+Omfu7j7/v385qku3vFyed5PEEPjbgCSEV3Wtca9ISQJAv4woUB+q3gp740U175ufSOj2GJTFqA5tbL29Bk3tc6z16+fD8X0vVkMlnM+p92aS9Kq2rKuqZzWyliXWQznPZbzmc/Zfgzmnf0iy/JDsix3pF76gGRqezlgVhTlzEIGWSoMzlLzBYpvNWmaTAZMaJRTXi3zZXV5Gy21jkxvxXRq3fnQ6KhHQqLfP8ihsaM81f8sjfZ63rP99dy8u423XL8273mX1V/M1ppNnPB0Ibl6uPP9u9i1sY7Tgz4+84MX+d5vj82rJvtsxbwCoRh2i7FgS7unVpVmL0AS9V+KD3d4lsyXIs9Rh4VZ6ncB98iyHAWCaNkvAOuA7kUaV9GSSWd01OfsG/OGMRp0lBVgxeLZqLVO5ar3+vqoMJdTZalAkiTufP+uRQv4mPUmXLZqzvj6uffY/Rh0Bt6z6W1YjWZef9WaWc+TJIm3rb+FrudO84uu37LhsnW8/+ZNXLO9kR893MmTBwc51uPmzvfvwqA/tx0yWy11XzCGs4D3ZtZSAWUiV73Y8KYWHs30qaczX4pZ1OdkqSuK0q0oyq7Uv/coirJbUZTrFEV5jaIog6ntLyiK8volHGtRMBTU/Ob1tvwLj6rLLMte1vVc2Iw2HEY7Jz2n8EX9rClvzYzRaTMtOI0xH82ORsKJCP5YgNe3vzqzKOlclJmcvFW+hVgyxv8pDwIgr6rkU++9hMs31TPmDXP49MSc3iufpZ5UVQLhWMHSGWH28ruZXqViVWnR4Inmt9RHijxHHcTio3mTWTpsyb7ZkWgCfyhWdK6XNLW2GiIJzYUxvTLjYpNehLShah3XNF8xr3Mvrt3K+soOTrhPcia1MlWv03HDJVre+rNHhub0PvmyX0KROKpauHRGAMcslRqdNiMGvSQs9SJitgqNo54QJmPxPY1PR4j6PPFGvDiM9qzqf1B8hbxmkm6YAdmVGRebXQ2Xcv2qq3n3xreik+b/8bqu5UoAHu97OrOtrd5JfZWNfZ1jc+qUlLbUbdNqqaeLeRXSUrfNUv9FJ0lUOs2MC0u9aPBEJjFIehyppyvQVoyPekK4KqxF9zQ+HSHq88QT8WY1aU5TrEHSNOlgqUlnzFjTS0G52ckta2/CaXIs6PyN1TIuazUvDO/DH9V8z5IkcfmmOmLxJC8q585hT/vUp1vqhc5Rh+mVGnOzvqrLLEwGosTiYgFSMeCJeCg3l2eJty8UIxxNFHWOOghRnxfheJhIIppf1FOWek2xWuqpJtStZS05/RaLCZ2k45rm3cSTcZ4eeC6zfdcmLTD97JHh2U7NkPGp66fuRaFXk4Lm49dJuhxLHchUxnQLF0zBSSQTTEb9s5bcLeYgKQhRnxcZf7opd+lwsVvqbWUtGHVGtru2FHoo52RXww7MehNP9D9DIqkV+HKlujEd73EzMXl24Qslwph0xqwfr0LXfQHticNmyC3qBVOiLlwwhWcy6kNFLbnqjGmEqM+DdJpTPkt9zKvd8GL1qVdaKvj81Z+ed/CyEFgNVnbWX4In4uXA2JHM9ss31aMCzx07u7VebLXUp2M32nJSGgGq0mmN5/jBEiw9mZK7ltLLUQch6vNitvrKoLlfdJJExSKuCFxsjDpDUQd4ppP+8XnszFTA9JL1teh1Es8cPnsWTDgezm04XUSiHoyHUFU1a3vaGBCiXnjcs3Y8SueoF6fhlkaI+jzwRme31Me9YarKzOh14k+6GNTba9lQtY4u7+lMeqPDamRrezV9owHOjPjznqeqKsF4qKgt9aSaJJzIFm/hfikevLN1PPKEkKCoSmvnQyjQPJitvVU8kcTrjxat66VUubZZq+48Pb3x8lTA9JlZctZjyRhJNTl7hcYC5xfbDfkXIKVLBQhLvfCcrTdpZZkZo6G4ZbO4R1dkeGe52ROTYVSKN0haqmyslqmZkd64bW01NrOBZ48MkUyqOefMVvclk6duWVANu0XDPssCJKvZgM1syGRRCQpHPjdrLJ7A44vgKnIrHYSozwtvZBK9pM98MdMUY3OMlYBO0nHtjPRGo0HPJetr8fijHO9155wzq6iHY9jMhoK7x2ypz45/lgyYCV8kx98uWF48ES8SEuWmKVEf82qGWzGXB0gjRH0eeCKTlJmcOSslx4o8nbGU2dWwA5PexJP9z2bE7vJNWt2dfC6YKVGfESgNxgrueoFpjTLyiHp1mZlINEFwDqtmBUuHJ+zFaXJkpcSWSjojCFGfM0k1iTc6mTfzZbhEUp1KEavByobKDtwRD76YFhztaKmguszMi8oo0RmNqvNVaFRVFX8oVvAgKczufgGoShkF6Sc/wfKjqiqe6GTJZr6AEPU5448FSKrJvD0Le4e1QvcttQtbGi84O3X2WgCGUmWPdZLEDrmWSDTBqYHJrGNDeYp5haMJEkm1KETdMUv5XZie1igyYApFIBYknoznZr6kDLfaClu+04oKIepzZLaFR6qq0jPso6bcUhSisRKpt6VFfaruy9om7UvXNeDNOjacp0RAsaQzAjiM2g//ZCw3JTOTASNKBRSM2daiTLlfhKW+YpjtZrt9EXzBGK31K6sXZDGR7jKVrmUP0J4W9f4Zlnoq/9tmnFahsYhEvcpSAcBEKDfIO5WrLkR9sfEGoqnyy2cPQs+azugNYTXri+IzdC4Km99VQmQsdVO2qPekXC+tdULUl4op98uUqFc6zVSXWTjZ70VV1cxK2VCqVZylSC11i8GCw2hnLDyes0+4X5aGQ6fG+a+fHgDAZNRRbjdR7jBT5TRz42WrWN0w9Z3OJ+rpkrv1lbaSWJEtLPU5MtvCo56hlKgLS33JMOtNVJorMj71NO1NZfhDsYy/E6YsdWuRld2dTo21momQm6SaXWa3wmlCkoSlvtj87lmtL+/mNVU0VNmJxpN09Xt5/tgI//XTA1mB6Xzf88lAlGgsWTKJEMJSnyPeWdwvvcOab3SVsNSXlHp7LccmThCKhzLpiu1N5Tx/bIST/V7qqrQAVr489eIT9Sq6J3vxRiapTLljQOvyVOEw4xaivmj0Dvs43uthU1slf/fm7ZntyaTKn17q475HOvnKg4f4p7+4GKNBn9fNWgot7KYjLPU54pml7kvPsI9Kp5lye/EW8loJZPzqeYOlU371vKIeLDJRt1QBMBbK74Jx+6IkkqJZxmLw8ItnALjhkpas7TqdxPU7mtm9pZ7uIR8//MMJLZ0xktubtJRy1EGI+pzxRiax6M1ZqXLeQBS3LyL86ctAvgyYlloHRoOOrv6pDJh8/Un94eIS9WprWtRzG2lXlZlJqipef3S5h7XimAxEee7oMDX1EZ7yP0RfqjBcGkmSeNeNMq31Tp46NMhj+/rxRLxYDRYsBnPmuFLKUQch6nPGG5nMsdLT+emr6kR++lKTDpYOBqf86ga9jtX1TvpG/ZnepcF4CIvenLXqN1AkxbzS1KRFPZwr6tUiA2bReGxfPwmDn3jrsxydOM7POh/KOcZo0PORN2zBYTVy3yOdTIS8OWtRpnLUhaW+Yogl4/hjgZybLYKky0d9StSHA9k9StubylFV6B7UXDDheDinQqOvyNwv1ZZqIL/7pUpkwCwKsXiSPx3qwrJ+LxE1RKW5gk7PKTrdp3KOrS638MHXbQLHGJFkGDvl7O8c47H9/fziyVMoZ9zoJClzb4odESidA5MinbHgOIx2nEZHlvsFpvLVT/Z72dBWRSgeznmiCoRimE16DPrisGEqLeXoJB3jeXPVRQnexWDP0V4izc+gMwd5VdsNbKyW+eLer/C77kfoqHx/zvFyawU1G7uZTMKR56s5HDiYtb+9qaxoPj/nYk6iLsvyTuBziqJcK8vyRuCbgAR0ArcrihKXZflVwKdS2/cCH1YUZUWUm5tKc5oh6kM+nDYjlU5zvtMEi0y9vZaTntNEEzFMes3qbp8WLFVVlXAiQv1MSz0UK2jD6ZnoJB3Vlsqz5qoL90s2w8FRvOPjlFN9zmOjiRgP9t2Pzu5jR80OXrP65UiSxPrKDo67O+nydNNe0ZZ1zvNDLzGZHGWVUaZlwyYqHWYqHGbKHSYqHGZqSyTzBebgfpFl+Q7g20D6m3In8AlFUXanXr9WlmUn8HngJkVRdgLdQM3iD7cw5Ot4FAjHGPOGaa1zlsSChJVAvb0OFZXh4GhmW7ndRE25ha5+L5F4hKSazKnQGAjFCtpwOh811mp8UT+RRHZAVLhf8nPPkfv4l0c/z+GxY2c9Lqkm+ereHxKzjOKMtvCeLW/KfD9fvfrlAPyu+5GscyKJKA91/R6jzsD7L72Vt9+wjlftauXyzfVsbKuiscZeMlY6zM1S7wJuAX6Yen2roigJWZZNQD3gBa4ADgFflGV5DfBtRVFG877bNCorbRgM+nMdBoDLVTgXR3xCs5pWueoy4xjo1Ka3YU31gsZWyPksNss1l7XuFp7sf4agfhKXS85s39xew2Mv9eFNZb5U2B2ZMYWjcaLxJNXl1jmPcznm01xZx7GJEyQtYVwVU9ZnjapiNumZDMYWZRwr4XOWVJMMBkdIqkm+e+RHfPLaj7GuZk3OcdFEjK89/wM6/cdJTFby0WvfS13tVBzM5drC5j6ZwyMKHt0YHdWrAbj/8K/xRie5ZeOrWNfSkvO+S8lS3J9zirqiKA/Istw27XVCluVW4BE0QT8AvBK4DtgO+IEnZVl+RlGUE2d7b7c7t/xoPlwuJ6OjvjkduxT0TWh+XF3ElBnHQUXb5nKa5z22Qs9nMVnOuThU7QvaOdSDbFuf2d5UrS08evGYlpMsJQyZMaV90ya9NKdxLtd87Ghf5s6BM1hj2W69KqeZ4YnAeY9jpXzO3GEPsUSMeoeLkcA4n338K/zdjg9lgucA/miAbx76Pl3ebpK+Cmo9V9FYbs+Z/w1N13J4ROG+fQ/xoW234Yl4+eWxP+I0Odhdc/my/r3O5/6c7cdgQc8UiqL0KIrSAXwd+BIwDrygKMqQoih+4Ak0gV8R5KvQmA6SrhKZL8tG+ks8OCNYml6EdGpESxHMV6Gx2Nwv1WdJa6wqsxAIxwlHRbMMgJHgGABXrLqEt8u3EogHuXv/tzMLhUaCo3xh7910ebsxB5qJHL+UG3e053WLdlS201GxhiPjx+mZPMOvTv2BaDLGa9fcmJM1VarMW9RlWX5IluWO1EsfkAReAjbLslwjy7IB2AUcXbxhFpZ8xbx6hnxYzQZcotvRslFuKsOit2RVawRorrVjMurom/AA2atJ0wHHYgqUwrRc9TwLkKozGTDCrw4wEtJEvcFRy+WNl3Lzmlfijnj4yv7vcGjsKF948SuMhsbRjXbgObKJq7Y0ZxqU5+NVbTcAcN/xB3hucC+N9noub7h0WeayHCwkpfEu4B5ZlqNAEC37ZUSW5X8C/pA65qeKohxerEEWGm9kEqdxqr1VKBJneCKIvKpCBEmXEUmSqLfXcsbXTyKZyNwPvU7H6voyTvr7MDHVys7rj/CjhzUP4LqWigKNOj81qVz18bPlqvvCNNbYl3VcxchIKjDe4KwFFV7Reh3eqI/H+57m6wfvQUJHvHsz8dFm3nZDBzfsaD7r93JdZTvt5W10ebsBuKXjppwWlaXMnERdUZRuNOsbRVH2ALvzHPN/wP8t5uCKgXQ9iDqbK7PtzIgfFbHoqBDU22rpnuxlLDSeWWUKsLa5nJMnNXeF1WAhFk9w988PMTEZ4Q1Xr2F9a2WhhpwXm9GKzWDNXyrAKTJgpjOastTrnbVEJrUyy2/seC2hWJh9w0fwH9+CJVLHX795M5tWV53z/SRJ4tWrX87/7v8Wm6rXs6Fq3VJPYVkRi4/OQTgRJpqM5fWni0VHy0/Grx4cyRL19sZydANaxUyH0c73fnucroFJdm2q46bLWwsy1nNRY61iMDCcVQ8eptwvolepxkhwHKvBitNkJ4J2j3WSjrLxS5l8vob6Kjt//dat1FfNvdXc+qoOPnbRB2h2NC7VsAvGynnmWCI8mSDpVGpUrygPUDDq8zTMAKivM6Cv6UefsHHiuJ5njw7T3lTGe1+1vmhdZNXWamLJOJPR7AyIdANqsapUS2ccD41Ta6vJuY9HeybQ63T887t2zEvQ06yrbM/qkLVSEKJ+DqZKcWZb6majnrrK4m9Cu9Kot+WW4AU4MPESkj5BdGAVv3yyh+oyMx+5ZSvGOa6DKARTJXizXTBTvUqF+2Ui7CGuJqi1Zq9lVFWVgbEAtZVW7JbiCoIXGiHq58A7o0RANJZgYCxIS50Dna44LcCVTLW1EqPOwPC0ao2xZJzH+p5GpxqJDjdjNun56zduK/oa91MZMNnBUqNBT5nNKEoFMBUkddmyRd3tixCKJGgSgeQchKifg5ntrfpGAyRVVfjTC4RO0lFrczEUGMm0g3txaB+TUR+bnNuxGMx84OZNtNQWfznkc+WqT0xGSJ6jUfJKJ53OONNSHxgLAIjsoDyIQOk5mJmjLoKkhafeVku/fxB32EuVpYJHzzyBTtLxlq0v5/2XlqMrUh/6TKbSGvPXVe8e8uELxor+iWMpGU0tPKqdYan3p0S9yVX8P97LjRD1c+Cd0V38UJf2qCyCpIUjEywNjjAUHGEwMMyldRdl9fssBaosFUhI56irHr6gRT1jqc8i6sJSz0W4X86BJzqJQdJjN9p44fgI+0+OsbapnCaX+DAVivpMv9JhHu19HIDrV11dyCEtCL1OT5WlYta2diAyYEaCYziM9pzKmwNjAfQ6iboSKom7XAhRPwfpNna+YIwf/kHBZNBx22s2lMwj/kok3a907/ABFPdJ5Mq1tDibCjyqhVFtrcYbnSSaiGVvz9RVv3AzYBLJBBNhd46Vns58qa+ylVRJ3OVC/EXOQlJNMhn1UW4q44d/VPCHYtxyTfuCcmIFi0etrQadpKPHp1VlvH7VNQUe0cJJpzVOzAiWTne/XKiMhSdIqklc1tzMl3A0QYNwveRFiDraL//d+7/Ntw/fm8moAPBF/STVJImomb3KKOuay7nhkuYCjlQAYNAZcFm1IGODvY6NJbzMe7bCXtXC/TItSOrK2p4JkgpRz4sQdWA87ObYxAn2jRzkF12/zWxPLzzqORPDZNDxXuF2KRrSLpjrW64u2hWjc6FmlrRGp92EQS9d0O6XdI56TpB0VIj62RDZL0CnuwvQcqAf7X2CJnsDOxt2ZHLUYyETb762XawgLSKua7mKSksFl9RfVOihnBc11vxpjTpJorrMwog7mFMb5kJhJJUVNNP9InLUz46w1IFOzykA3rvp7VgNFu5THqBn8gz7u/sAqHdW8rIdwu1STHRUruFN616HUVfadkn1LKUCAFbVOQmE44xdoIW90u6XtKstTX8q86WUmkEvJxe8qKuqygl3F3ajje2uzbx309tJJBN84+D32d9/EoCbLlsv3C6CJcFutGHRm/Pmqrc1aGsheoZKvyXdQhgJjVFucmIxmDPbVFVlYDxAfbXIfJmNC/6vMh5244546KhYg07Ssal6Pa9rf5WWZlbWA0Bbtesc7yIQLAxJkqi2VjEWnkCdURKgrV5bxXx6aLIQQysosUQMd9iTEyQdnwwTiYqaL2fjghf1tD+9o6I9s+2GVdfQap5qbDy97K5AsNjUWKuJJqL4Y4Gs7elSFN2DF56lPhoaR0UV/vQFIEQ95U/vqFyT2SZJEuUTl5DwVVBjdmHWX7jLtAVLz1QJ3mwXjM1ioK7KRveQL8eKX+mMzlIeYGAsCEBjtRD12bigRT3tT3cY7TSklp6ntx/vmcTccyWf3PW3BRyh4ELgbE2oV9c7CUXijHhCyz2sgjKSDpLm1HzROh+JMh2zc0GL+nh4AnfEw9qUPz3N4HgQrz/KxrZqDPrSzq4QFD/VqeyOfKLeVn9humBGz1Jy16AXmS9n44IW9RPuXNcLwNFu7cu1sciaFQtWJlMLkHIzYNLVQLsvsGDpSHAMCSkrnTGZVBkYC1JfZUOvu6Cl66xc0H+ZTo8WJF03LUgKcLTbDcCGNiHqgqWnylKJTtIxHBjN2beqzonEhWepjwTHqDCXY9RPtaob9YSIxBIiSHoOLlhRV1WVTvcpHEZ7pj43QCKZRDnjprbSSk25eMQTLD1GnYFGez19/n4SyUTWPqvZQH21jZ5h3wXTBSmSiOKNTuYESXtTTysinfHszMlhLMvyTuBziqJcK8vyRuCbgAR0ArcrihKXZfnLwJVA2qR4naIo3qUY9GKQ9qdvd23J8qd3D/oIRRLs3FhVwNEJLjRay5rp8w8wEBimxdmYta+tvozB8SGGJ4I0XABZH6OzBEl7U4uwGmtEt6OzcU5LXZblO4BvA5bUpjuBTyiKsjv1+rWp/+8AblQU5drUf0Ur6CD86YLiorWsBYCeyd6cfemVpd0XyMrSdLejuhlB0t5UK0mR+XJ25uJ+6QJumfb6VkVRnpBl2QTUA15ZlnVAB/BNWZaflmX5tiUY66Iymz/9WI8bCVgvRF2wjLQ606Lel7NvdWpl6YXiV58tnbF3aBKDXsJVYcl3miDFOd0viqI8IMty27TXCVmWW4FHAC9wALAD/wt8CdADf5Zl+UVFUQ6e7b0rK20YDPo5DdTlWryeoKqq0jV5GqfZwda2tZkKeOFInJP9k6xpLmf1qqV1vyzmfArNSpoLFGY+VdVrMb1kpD/Yn3N9Z7kVnQT944F5j60U743vlPaQv76pFVeZNv5kUuXMiJ/mWif1dStnhfdS3J8FJWEritIDdMiyfDuakN8GfFlRlCCALMt/ArYBZxV1tzs4p+u5XE5GRxfPShkLjTMedHORawtjqcUMAIdPjRNPJFnXVL6o15vJYs+nkKykuUBh59PsaKJ7spe+ofGcVcyNNXZO9nkYHp5Ep5tbcblSvTe97kEkJKSQmdGINv5RT4hINEFdpbUk55SP87k/Z/sxmHf2iyzLD8my3JF66QOSwDrgaVmW9bIsG9ECpi8tYKzLwpQ/fUYqY4+WyrixTQRJBctPa1kzSTXJGV9/zr62+jKisSSD44E8Z64sRoNjVFurMEwrq9wvar7MmYWkNN4F3CPL8p+Bd6EFTY8BPwSeBR4HfqAoypHFG+biciJTxCs3SGrQS6xtXjmPd4LSoS3lV++dPJO77wIJlobjYXwxf04N9QHRwm7OzMn9oihKN7Ar9e89wO48x3we+PxiDm4pUFWVTk9uvRdfMErvsJ/1qyowG+fm5xcIFpNV6QwYX26wtHVauYDdWxqWdVzLyXhYe1qumdkYQ7SwmzMX3OKjibAHT8TL2oo1WS3Cjvd6ANggXC+CAuGyVmMzWOnOY6m3uBzodRLdwyu7XMBoqlJlunRCmoHxAEaDDleFWBB4Li44UR8IDALkLPDI5KeL0gCCAiFJEq1lLYyFxgnEspMITEY9TTV2eof9JJLJrH2d7lN8+/C9hOKlX8kxXX44XY44zag7RH21fc5B4guZkhT1I+PHuXv/t4kkovM+d8A/BECjvT5r+9HuCaxmQ6YqnkBQCFqdWi/cnln86rF4MlNTPM1TA8+yb+Qgzw7uXZYxLiXpBtzV09wv/lCMYCROfbVo/D4XSlLUT3t7OTZxglPe7nmfOxBIibpjyi/p9kUY9YRZ11wuqr8JCsrUytJcv3pbZhFStgumzzcAwDODL5R8M410+eHp7pfRVC35C6FEwmJQkgrWlBLk9Id5Pgz4hzDrTVRZKjLbTvZrix1E1oug0GRE3Te3cgHRRIzhoFbdsd8/SG+eIGspMRYex2G0YzVMrRodcWuiXi9EfU6UpKg3OzR/eJ9/fqIeT8YZCo7QYK/PKuLV2ecBoKO5YrGGKBAsiHJzGRXmcronz+RY3U01Dgx6Kau2+mBgCBWVulSD5j2DLyzreBeTpJpkIuSmekaQNGOpi8yXOVGSol5trcSiN9PnH5zXecPBUZJqMseffrLPi0EvsbpB+NMFhae1rAVf1I8nkl0Tz2jQ0exycGbETzyhBUvTT6sva7mKCnM5Lw7tJ7qAWFMx4Il4iauJnCBpupVfXZXwqc+FkhR1naSjydHAcGCEaCI25/MG00FSx5Soh6Nxeof9tNY7Mc6xDo1AsJS0ZYp75QmW1juJJ9RM3nb6aXVVWTO76ncQToTZN3Jo+Qa7iEz507Nz1EfdISQQgdI5UpKiDtDsbERFZTAV+JwL/aljm6aJ+umBSZKqSkdTxWIPUSBYEKvKtAyYfPnqM9vb9fkH0Ek6Gmx1XN54KQB7Bp9fppEuLvmCpKBZ6pVlZmF0zZHSFfW0X30ewdKpdMapzJdOESQVFBmr0mmNeYKe6QyYniEfSTVJn3+QelstRr2RGms16yraOek5zUgwtzVesTOeWXg0ZanH4gk8vgi1YtHRnCl9UZ+HX30gMESZyYnDNBVwOdmXEvUmIeqC4sBmtFJnc9E72UdSzV5o1OSyY9BLnB7yMRYaJ5qI0jxtId0VjZcB8Mzgi8s65sVgLJxrqY95w6hAjRD1OVOyot5gr0Mn6TI+xf5RP1/8yX7GPPlX1YXiYSbC7qwgaTKp0jXgpa7KRpndlPc8gaAQrHK2EE6EMw0j0hj0OlpqHfSP+umZ1Ko5pg0cgG2uzVgNVvb0v8C/fuvpTJCxFBgLTaCX9FSYpwysdDqjsNTnTsmKulFvpN5WS79/gKSa5KXOMY6cnuAPz+f6IYGM7316kLR/LEAokqBDWOmCIqOtbPZgaWt9GfGEyvGRHiBb1E16I5fWXYQ/7ufA0FH+8HxuvnuxMhYap9pSmZVunE5nrK0Uoj5XSlbUAZocjUQSUcZCE3h8EQD2HBkiEkvkHJuvPEA6P1340wXFRmtZ2q+ePwMGoNurWepNzuyqjWkXjMHVx3NHhonFc78PxUYoHsYfC+TkqKefNEQhr7lT0qLenPow9/kHcKdEPRSJ8+LxkZxjB/JY6ml/eocQdUGR0exoRCfp8mbApEV9PDpChbkchzF7UU6NqZZkoAxdxSjBpJ99nWM571FsjJ8lnRGEqM+H0hb11GNnv08Tdb1OQgIeP5CbETPgH0JCyqqh3tnnxWE1Ui8WNQiKDKPeyOqyVfRO9uUsQmqssWMwx4hJwSzXS5rOPg/x0WYkScXg6uOpQ/NbpFcI8gVJQbPUbWYDDquxEMMqSVaEqPf5B3D7I1SXW9i4uoqTfV76R6d6j6qqyoB/iBprFaZU70e3L8L4ZJi1TeVZddUFgmLhkrrtqKi8NHwga7tBr6O2QVt0Nz09N83xHg+JsUZMOjPmhn6OdI9lnmSLlbE86YxJVWXMG8Yl/OnzoqRF3WGyU2Eu54xvAF8gSqXDzDXbNKF/4sCUdeKNThKIB7MqM07VexGuF0FxclHtVnSSjheG9+Xsc1ZrbglLMrf+//FeN3qMXLv6cpL6ELqKYfYcLm5rPeN+mVYiwOuPEosnhetlnpS0qAM0OxrwRidRDVEqy8xs76ihzGZkz+HBTIAoX5A0k58uRF1QpDhNDjZUraPX189wYEacyKqtKI1NOrI2B8NxeoZ9tDeW8Rr5OgCM9b08dWho1rK8yWThy/WOZeqoT4n6iFurGy/SGefHChB1zTLX2SapdJgx6HXs3tpAIBznRUVbVZcvSNrZrxXxEk0xBMXMpXUXAfDC8P6s7QFpHDWhZ3Q0+yt84owHVQV5VSUNzlo2Va9HcrgZiQzS1Z/bCq9/1M8/fG0P9z1yYsnmMBfGQnlK7op0xgVR8qLe5EyLuo8KpxmAq9MumP1awDRtqTelLPVwNM6ZYT9t9WWinoSgqNlSsxGTzsgLw/sylnY0EWMiMo4aLKNnWm110FwvAOtbNbfMtc1aj3hDXU9OwNTjj/Df9x/A7Yvw6It99A5nv9dykVSTjIfzldwNAyLzZb6UvKinLXUpZakD1FXa2NBaiXLGw+B4gIHAEAadAZetBoBTqSJewvUiKHYsBjNbXZsYC41nctbTNdQdVNM/GsjKQz/e68agl2hv1GrErK/qoNZag6FqiOc7ezNrOMLROF++/yDjkxEu6qhBBR54/NSyzw+0krsJNYFrZjpjJkfdku80wSyUvKjXWKvQY0Rn81FZZs5sv2a7JvaPH+hjKDBMg602s1JN5KcLSomMC2ZIC5imi9jV2+pJJFX6UmV4A+EYZ4b9tDeWYzJqT6A6Scc1LbtBlyRe0cNLyiiJZJKv//IIPcM+rt7WwEdu2cL6VRUcOjXOsR73ss9vLE+QFLQSAXqdRJVTiPp8KHlR10k6zPEKJGsAh21qOhd1uHBYjTytnCKWjGdnvvSLIl6C0mFD1TocRjt7hw+QSCYy9Y7WVmmlBNLt7U70elABeVVF1vm76ndg0pkw1Pby5KF+fvRwJwe7xtm8uoq/eIWMJEm86bq1APzssZPL3ud0LE+zadAs9ZoKKzqdSDmeD3MSdVmWd8qy/Fjq3xtlWX5KluWnZVm+R5Zlw7TjdLIs/06W5b9aovHmRRcpR5JUgtKUlWE06Ni9pZ6QTtt2XInzpZ/u56sPHqKzz0N9lQ2nTRTxEhQ/ep2ei2u34ov5OeHuos8/iE7SsaWxDYCeVG31470eADa0Zqc5WgwWdjdehmSK0Ok7zmP7+ml2Ofjg6zdj0GsSsLqhjEvX13J60JdJMFgupkruTlnqoUgcfygmMl8WwDlFXZblO4BvA+lnoDuBTyiKsjv1+rXTDv8MkJs4u8TEfFpa18yGGddf3IyzSgu2jA4ZOHxqgheVUaKxJFvbq3PeRyAoVi5JuWCeH36Jfv8A9bZaVtWWYzTo6B7ULPXjvW6MBh1rUv706VzdfDkA+roeKp1m/uZNW7GaDVnH3HL1GvQ6iQce78q0y1sORvOI+ohb+NMXiuHch9AF3AL8MPX6VkVRErIsm4B6wAsgy/IbgSTw+6UY6GyoqkrAbcNQn9uIuqbCysb1BvaPwn++95XY9A4isQSxWDLL/y4QFDtryluptlTy4vB+kmqSJkdDpgxvz5APty9C34gfeVVF3oyuWpuLjVUyR1F4+821VJXlimVdlY2rtzfy55f6efLAANdd3LwcU2MsnFtyN1OdUVjq8+acoq4oygOyLLdNe52QZbkVeARN0A/IsrwZeDvwRuBf53rxykobhjmmFLpc+fPJvf4IMb8dgyoxHB7OOW44NILdZKOjuamoygHMNp9SZCXNBYp3PletvoxfHPsDAHL9alwuJ+vbqjg1MMmzx0dQgR0b67PGP/3fb9jyCo4+rvD4+B+5ftsWjPrceirvvXkzzxwe4lfP9PDaaztyrPmlYCLiptZeTV3tlKgHD2lP3Wtbq2adz0pgKeazoDumKEoP0CHL8u3Al4BhoAn4E9AGRGVZ7lYU5axWuzu1YuxcuFxORkfz59D2DvtA1WOlnG53H8Mj3kyWSzQRZcg/SntFG2Nj/rznF4KzzafUWElzgeKezybnJn6BJuqVUjWjoz7qUu6J3zx1GoCWaltm/DPn0qBrZmf9Dp4b2svXnrmPt8m35L3OKy5t4aGnu7nvt0e5+crVSzklQvEwvoifFntT1lhP93sAMOuYdT6lzvnM52w/BvMWdVmWHwI+rihKJ+ADkoqi3DFt/78BQ+cS9MXC49cKFVUaXAwkOvl996OpLkceRkNjqKh5ix4JBKVGo6OeJkcDA/4hmlLZXOmepcFIHJNBx+qGXH96GkmSeKv8Bvr8AzzV/yxrylrZ2bAj57gbL1vFo3v7+NO+fm7a3YZuCZ9wx2drNp3yqYs2dvNnIZb6XcA9sixHgSBw++IOaX5MpKrP1VsbGfB38pvTD2f2GXUG6u11XFq/vUCjEwgWl/dsfBujoXGcJi05oLHGhtGgIxZP0t6kBU7Phklv4vbN7+RzL/wPP1Z+TrOzMfMDkcZqNrC9o4anDw3RM+Q76w/F+ZIuuZu7mjREucOE2ShWfM+XOYm6oijdwK7Uv/cAu89y7L8txsDmSrrj0SU1O1jXVI3NYKXKUkm1tRKn0VFUfnSB4HxpdNRn1TDS63SsqnXQNTCZKQ1wLmptNbxr41v45qHv861DP+COS/4amzHbIt7aron6wa7xpRX1PCV344lkpiy2YP6U/OKjdJ3o2nInVzXtYkfdNlaXr6LM5BSCLrggaE+J36a2qnMcOcU21yZe0Xodo6FxfnjspzkLjja1VaHXSRzsWtquSflWk45PhlFVUfNloawYUa90ihRFwYXJzbtX8w9vuyhvfvrZuGn1K1hX0c7BsSM81vd01j6bxUBHczmnB314A9HFHG4WY3ly1NMt7EQ648IofVH3R7CaDVhMS596JRAUIzaLIWcV6VzQ6/TctvkdGHQGnhl8IWf/1natAN7hU+PnPcbZGA9N4DDasUwruZsp5CVK7i6Ikhd1jy8irHSBYIE4TQ7WlLXS7x/EHwtk7Uuvuj7QtTSini65O7PZ9IhYeHRelLSoR2IJAuE4lQ5Rw0UgWCjrKtsBOOnOLr3bUG2jptzCkdPjxBNJjk908kjv48SS8UW5bpenO2/J3akSAULUF0JJi3o686VCWOoCwYLpSIn6CU9X1nZJktjaXk0okqDzjId7j93Pgyd/wxdfvJuR4PkV/UokE/z0xC8AuKrp8qx9o54QZpMepy13xavg3JS0qE8FSUXRH4FgobSVtWDSGVHcXTn70n71Z0514o54cBjtnPEPcNcLX+b5oZcYHA/w8Itn5l0A7NHeJxgIDLG7cSftFW2Z7WOeEEMTIeoqrCJ7bYGUdHTR7ReZLwLB+WLQGVhT3sZxdyeTUR9lpqkl6OtXVWAy6Dgyfgyq4Y0dNyMBP1Z+zveP/h8G7yp8nesYmgjyzlfIc7reaHCc33Y/jNPk4PXtr8psjyeSfOOhI8QTSV5+actiT/OCoaQt9bT7Jd3GTiAQLIy0X71zhrVuMurZ0FpJ0NyPhI5N1TKX1F/EP176MexqDfHyXiybnuXP+3r500t957yOqqr85MSDxJJx3thxMzajLbPvl0+dpmtgkl2b6rhic/1Z3kVwNkpa1CdEjrpAsCisq9Q6H53I44JZt8aKZPdSrW/IiPD4iI6xFy/GGGhEsvqx1/i57+FOjnRPnPU6Lw7v59jECTZUrWNH7bbM9mPdE/z2mR5cFRbemerGJFgYJS3qHiHqAsGisMrZhEVvzgmWAugrRpEkUL11gNaV6Hu/O44OPTd27ATgysvs6HTwtQcPMzSRv/pqIBbkgc5fYdQZeat8S0a4fcEo3/z1UXQ6ifffvGlZyv2uZEpa1N3+CHqdhENEyQWC80Kv09NesZqR4BieiDdr3+nASQCGe5xEYgnuf6yLMW+YV+1axUUtawCI6j28+5XrCUbifPlnBwmEYznX+GXXb/HF/Lx69Q2ZFaSqqvLd3xzD64/yhqvX0N4o6r2cL6Ut6r4IFQ7zkpYGFQguFNJ+9ekumFgixvGJE1gpJxaw8vPHT/HYvn6aXHZu3r0al7UGo85Av3+A3VsaeNXOVQxPBPnqg4dJJKcyYvr9gzw98DyN9nqub7k6s/2RvX0c6BpnY1slr9y5avkmu4IpWVFPJlW8/qhwvQgEi8S6ilxRV9wniSZjbKzcAMDDL55BJ0m87zUbMBp06HV6Gu0NDAaGSSQT3HpNO9vX1nCsx82Lx6dy2Y9PdALw8tZr0eu0crreQJT7/3wSh9XI7TdtFMbZIlGyou4NREmqqhB1gWCRaHY2YjVYs0T90PgxAK5s3Ybdovm6X315a6Y5B0CTo4G4mmAoOIJOJ/H6q7RuSdMrPHZP9gJar9U0B7vGiCdUXr2rlQqRwbZolKyoe0SOukCwqOgkHWsrVjMenmA85EZVVQ6PHcNusNFe0cYrd67ioo4abt7dlnVek1NrstHvHwSgpdZBucPEoVMTJFMlfbsnz+Aw2qmeVmL3YKqmzLa12WUCBOdHyYr6xGSqRID4hRcsEvF4nHe+85381V/dxuTkZEHG8MADPynIddOsm1Yy4Iy/H0/Ey8bq9eh1el5zeRsfvXUrBn22bDQ7GgHo8w0AWnmBLWuq8YdidA/6mIz6mAi7aS1ryWS8xBNJjpyewFVhob7KhmDxKNncIWGpr1x++qeTvHB8ZFHf89L1tbz5ZWvPeszY2BiBQIBvfOP7i3rt+fD973+XW299S8GuL6fy1TvdXUyE3QBsdW086znpdnhpSx1g65pqnjo4yKFT46w2aimObWVTq0Q7+7yEowl2b2kQOemLTMmKumiOIVhsvvCFO+nu7uY///P/Y2xslEAgQCKR4C//8oPs2HEp73znm2lpacVoNPAP//DP3HXXv+P1aul/f/M3/0B7+1p+/etf8OCDD5BMJrjyymt43/s+wAMP/ITHH/8zoVCIiooK7rzzCwwODvDZz34avd5AMpnkU5/6DL///W+YnPTyhS/cxd///f8ryN+gwV6H3WjjhLsLZ2AIvaRnQ9W6s55jNViosVTR5x9AVVUkSWJjWxU6SeJg1zhSg+Zbbyubym45lHa9tAvXy2IjRF1QdLz5ZWvPaVUvBR//+P/jM5/5JDabnUsuWcOb3/w2RkdH+NCHbuenP/0loVCI97znfaxbt56vfvV/2LHjMt7whjdy5kwvd975ae688/Pce+/3+f73f4zJZObrX7+bQMCP1+vlv//7q+h0Ov7u7z7CsWNH6Ow8wYYNm/jQhz7GgQP7CAT8vPvd7+OBB35aMEEHza/eUdHO/tFDuCMe1ld2YDWcu2Bek7ORA6OH8UYnqTCXY7MYWNtcTucZD3Z3N5BtqR/oGsNk1CGvqliimVy4lLCohwHhUxcsPj09p3nFK14JgMtVi81mx+3Wlr+vWtUGwKlTJ3nppRd59NE/AuDzTdLf38/q1e2YzZoIfvCDHwXAaDTyb//2z1itVkZGRojH49x00+v40Y++z8c//lHsdgcf+MCHl3mWs7OuUhN1gM01G+Z0TpOjgQOjh+n3D1Jh1hYQbW2v5sQZN72+PmptNZkSA6OeEIPjQbavrcFo0C/NJC5gSjZQ6vZHcViNGA0lOwVBkdLaupoDB/YDMDo6gs83SVmZJlRp/29raxtvfvPbufvub/If/3EXr3jFq2hqaqa3t5toVOvp+S//cgf79u3liSce49///bP87d/egapqC3Keeupxtm27iC9/+Wtcd931/OhHmh9/ZgPoQpAOlgJsqTm7Pz3NzGApwJY11UiWADE1muV6SWe9bBGulyWhJC11VVXx+CLUih6GgiXgXe96L5/97L/z2GOPEolEuOOOf8ZgMMw45jbuuus/eOihnxMMBrjttvdTWVnJO97xbj7ykfcjSRK7d1/Fhg2bsFqtfPCDtwFQXV3D2NgomzZt4TOf+RTf//53SCaTfPSjfwdAW9tq/v3fP8m//ut/LPu809Tbaqm1apb19IbQZ6M5T7C02WXHURMgDrQ6p1wvaVHfukaI+lIgFdIyGB31zeniLpeT0VFf5nUwHOcj//0EW9ur+Zs3bTvLmcXJzPmUMitpLrCy5nM+cwnGgkiSbk7+dNAMrX948lOUmcr4111/n9n+bw9/h1G9wttb38vu9g1EYgn++stPUltp5T/et3NeY1pJ9wbObz4ul3PWlKE5WeqyLO8EPqcoyrWyLG8EvglIQCdwu6IocVmWPwy8B1CBLyiK8tMFjXYOpP3pIkgqECwN0+uczwVJkmhyNNDl6SaaiGLSa32DkxY3akTHUJ8B2uF4j5tYPJlpai1YfM7pkJZl+Q7g20D6J/tO4BOKouxOvX6tLMs1wAeBK4DrgS/KsrxkyaeZjkciSCoQFA1NjkZUVAYCQwBEE1HcsTHUYBlHTnsAOHhKuF6WmrlY6l3ALcAPU69vVRQlIcuyCagHvIqijMmyvD1lsbcBYUVRzulaqay0YZhj9NvlmmqxlTitZSK0NJZnbS8lSnXc+VhJc4GVNZ/lnMsG32oe73saL25crk0cHz1JkiRVhnq6h3wYLEaOnJ7AbjVy+fZm9Pr5JzmspHsDSzOfc4q6oigPpIQ6/Tohy3Ir8AjgBQ6ktsdlWf4I8Gngf+Zycbc7fzH9mcz0PfUOaAs+jKgl6WNbSb7BlTQXWFnzWe65lKtaUPX44Cm2lW1jX+9xAFaXtzAI/Pj3xxhxh7hsQy0TE4F5v/9Kujdw3j71WfctKB9QUZQeRVE6gK8DX5q2/W6gAbhaluXrFvLecyEa19LCRPaLQFA8NNjr0Ek6+lIZMD2TZwDY1bYegIdf0F5vEa6XJWXeKY2yLD8EfFxRlE7AByRlWZaBzwK3AjEgAiRnf5fz49W7WtmyppraSlEISCAoFox6I7U2FwP+QZJqMlOZcWNDE1VlvUxMRpAQor7ULMRSvwu4R5blPwPvQguaKmhumGeAPcCziqI8vnjDzMZqNrCupWKp3l4gAOC3v/0VX/va/xZ6GCVFs6OBcCJC92QvE2E3bWUt6HS6TGC0raGMMrupwKNc2czJUlcUpRvYlfr3HmB3nmM+jeZPFwjOi5+f/DX7Rg4t6nteVLuFW9betKjvKcil2dHIi8P7ebr/eWCqiNdF61w8tn+Ai9fVFHJ4FwQluaJUIFgKIpEwf/u3/0pPzxlisRjXXXd9Zt+Pf3wvjz76R/R6Pdu2XcSHPvTXHDy4n7vv/m8MBgMWi4XPfOZzmExmPv/5O+nrO0MymeQv//KDXHzxJQWc1fKSLhewd+QAMCXqW9ZU809/cTGrG8pmPVewOAhRFxQdt6y9qSBW9S9+8QBNTU184hP/zpkzvTzzzFP4/X66uk7ypz89zNe//l30ej3//M938PTTT7J//0u87GU38OY3v52nnnqCyUkfzzzzO8rLK/inf/pXvF4PH/7w+7n33iVbh1d0pLsgxZIxAFrLmjP7OporCjGkCw4h6gJBit7eHl7xCs06b2lZxaFDTsbHx+np6WbTpi2Z+i/btm3n9Oku3vnO9/KDH3yXj33sg7hctWzcuJmurpMcPLiPo0cPA5BIxPF4PFRUVBRqWstKmcmJ0+TAF/VnVWYULB+ixKFAkKK1dTWHDmm+/P7+Pr7xja+ktrdx9Ohh4vE4qqqyf/8+Wlpa+eMff8urX30T//u/32D16jU89NDPaW1t44YbbuTuu7/JF7/4P1x33Q2UlV1YLoe0C2Z6ZUbB8iEsdYEgxetedwv/9V+f5SMfeT+JRIK3vOUdeL0e2tvX8rKX3cAHP/g+VFVl69ZtXH31tRw9eoS77voMVqsVSZK4445/pqbGxec+9xk+8pH3Ewj4ecMb3oROd2HZTs2ORo5NnBCiXiBKskpjqbOS5rOS5gIraz6FmstQYISHTv2et8m34DQ5Fu19V9K9gQJXaRQIBIK5Um+v5f1b3lXoYVywXFjPhQKBQLDCEaIuEAgEKwgh6gKBQLCCEKIuEAgEKwgh6gKBQLCCEKIuEAgEKwgh6gKBQLCCEKIuEAgEK4iCrigVCAQCweIiLHWBQCBYQQhRFwgEghWEEHWBQCBYQQhRFwgEghWEEHWBQCBYQQhRFwgEghWEEHWBQCBYQRR1kwxZlnXAV4FtQAS4XVGUk4Ud1fyRZXkn8DlFUa6VZXktcA+gAoeBDyuKkizk+OaKLMtG4LtAG2AGPgMcpXTnowe+Bcho4/8rIEyJzgdAluVaYC/wciBOac/lJWAy9fI08A3gy2jz+qOiKJ8u1NjmiyzL/wTcDJjQNO1xlujeFLul/nrAoijK5cD/A75Y2OHMH1mW7wC+DVhSm74E/IuiKFcBEvC6Qo1tAfwFMJ4a+yuBuynt+bwWQFGU3cC/AP8fJTyf1I/uN4BQalMpz8UCSIqiXJv6773A14G3A1cCO2VZvqigg5wjsixfC1wB7AauAVpYwntT7KJ+JfB7AEVRngUuKexwFkQXcMu01zvQfqUBfgfcsOwjWjj3A59M/VtCs5hKdj6KovwCeH/qZSvgoYTnA3wBTfgGUq9LeS7bAJssy3+UZflPsixfDZgVRelSFEUF/kDpzOdG4BDwIPAr4Ncs4b0pdlEvA7zTXidkWS5ql9FMFEV5AIhN2ySlPpQAPqB8+Ue1MBRF8SuK4pNl2Qn8DM26Ldn5ACiKEpdl+fvA/wI/okTnI8vye4BRRVH+MG1zSc4lRRDtR+pGNLfY91Lb0pTSfGrQDNI3oc3lR4Buqe5NsYv6JOCc9lqnKEq8UINZJKb7zZxo1mHJIMtyC/Bn4IeKotxHic8HQFGUdwPr0Pzr1mm7Smk+twEvl2X5MWA78AOgdtr+UpoLwAngXkVRVEVRTqAZd1XT9pfSfMaBPyiKElUURUGL20wX8UWdS7GL+tPAqwFkWd6F9ghT6uxL+dgAXgU8WcCxzAtZluuAPwL/qCjKd1ObS3k+70wFsECzApPAi6U4H0VRrlYU5RpFUa4F9gPvAn5XinNJcRupGJosy42ADQjIstwuy7KEZsGXynyeAl4py7KUmosdeHSp7k2xuzIeRLM+9qD5cN9b4PEsBh8HviXLsgk4hubGKBU+AVQCn5RlOe1b/xjwPyU6n58D35Nl+QnACPwN2hxK9f7MpJQ/a98B7pFl+Sm0DJHb0H50fwTo0bJfnivg+OaMoii/TsUEnkczpD+Mls2zJPdGlN4VCASCFUSxu18EAoFAMA+EqAsEAsEKQoi6QCAQrCCEqAsEAsEKQoi6QCAQrCCEqAsEAsEKQoi6QCAQrCD+fzTxbZYTDHEGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"# Actual vs Fitted\\nresult.plot_predict(\\n    start=1,\\n    end=60,\\n    dynamic=False,\\n);\";\n",
       "                var nbb_formatted_code = \"# Actual vs Fitted\\nresult.plot_predict(\\n    start=1,\\n    end=60,\\n    dynamic=False,\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Actual vs Fitted\n",
    "result.plot_predict(\n",
    "    start=1,\n",
    "    end=60,\n",
    "    dynamic=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = int(len(df) * 0.8)\n",
    "train = df.close[:n]\n",
    "test = df.close[n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(train))\n",
    "print(len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "step = 30\n",
    "\n",
    "model = ARIMA(train, order=(6, 1, 3))\n",
    "result = model.fit(disp=0)\n",
    "\n",
    "# Forecast\n",
    "fc, se, conf = result.forecast(step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc = pd.Series(fc, index=test[:step].index)\n",
    "lower = pd.Series(conf[:, 0], index=test[:step].index)\n",
    "upper = pd.Series(conf[:, 1], index=test[:step].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "plt.plot(test[:step], label=\"actual\")\n",
    "plt.plot(fc, label=\"forecast\")\n",
    "plt.fill_between(lower.index, lower, upper, color=\"k\", alpha=0.1)\n",
    "plt.title(\"Forecast vs Actual\")\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auto ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pmdarima package provides an auto_arima method that uses a stepwise approach to search multiple combinations of p,d,q parameters and chooses the best model that has the least AIC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima.arima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = auto_arima(\n",
    "    df.close,\n",
    "    start_p=1,\n",
    "    start_q=1,\n",
    "    test=\"adf\",\n",
    "    max_p=6,\n",
    "    max_q=6,\n",
    "    m=1,  # frequency of series\n",
    "    d=None,  # determine 'd'\n",
    "    seasonal=False,  # no seasonality\n",
    "    trace=True,\n",
    "    stepwise=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
